OpenRaiser/Intern-Atlas huggingface dataset:
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Venue: arXiv cs.CL 2018
Year: 2018

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.
Grounding Visual Explanations
Venue: arXiv cs.CV 2018
Year: 2018

Existing visual explanation generating agents learn to fluently justify a class prediction. However, they may mention visual attributes which reflect a strong class prior, although the evidence may not actually be in the image. This is particularly concerning as ultimately such agents fail in building trust with human users. To overcome this limitation, we propose a phrase-critic model to refine generated candidate explanations augmented with flipped phrases which we use as negative examples while training. At inference time, our phrase-critic model takes an image and a candidate explanation as input and outputs a score indicating how well the candidate explanation is grounded in the image. Our explainable AI agent is capable of providing counter arguments for an alternative prediction, i.e. counterfactuals, along with explanations that justify the correct classification decisions. Our model improves the textual explanation quality of fine-grained classification decisions on the CUB dataset by mentioning phrases that are grounded in the image. Moreover, on the FOIL tasks, our agent detects when there is a mistake in the sentence, grounds the incorrect phrase and corrects it significantly better than other models.
Fast & Flexible IO : A Compositional Approach to Storage Construction for High-Performance Devices
Venue: arXiv cs.SE 2018
Year: 2018

Building storage systems has remained the domain of systems experts for many years. They are complex and difficult to implement. Extreme care is needed to ensure necessary guarantees of performance and operational correctness. Furthermore, because of restrictions imposed by kernel-based designs, many legacy implementations have traded software flexibility for performance. Their implementation is restricted to compiled languages such as C and assembler, and reuse tends to be difficult or constrained. Nevertheless, storage systems are implicitly well-suited to software reuse and compositional software construction. There are many logical functions, such as block allocation, caching, partitioning, metadata management and so forth, that are common across most variants of storage. In this paper, we present Comanche, an open-source project that considers, as first-class concerns, both compositional design and reuse, and the need for high-performance.
Vehicle Communication using Secrecy Capacity
Venue: arXiv cs.IT 2018
Year: 2018

We address secure vehicle communication using secrecy capacity. In particular, we research the relationship between secrecy capacity and various types of parameters that determine secrecy capacity in the vehicular wireless network. For example, we examine the relationship between vehicle speed and secrecy capacity, the relationship between the response time and secrecy capacity of an autonomous vehicle, and the relationship between transmission power and secrecy capacity. In particular, the autonomous vehicle has set the system modeling on the assumption that the speed of the vehicle is related to the safety distance. We propose new vehicle communication to maintain a certain level of secrecy capacity according to various parameters. As a result, we can expect safer communication security of autonomous vehicles in 5G communications.
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario
Venue: arXiv cs.HC 2018
Year: 2018

Being able to recognize emotions in human users is considered a highly desirable trait in Human-Robot Interaction (HRI) scenarios. However, most contemporary approaches rarely attempt to apply recognized emotional features in an active manner to modulate robot decision-making and dialogue for the benefit of the user. In this position paper, we propose a method of incorporating recognized emotions into a Reinforcement Learning (RL) based dialogue management module that adapts its dialogue responses in order to attempt to make cognitive training tasks, like the 2048 Puzzle Game, more enjoyable for the users.
Using the Value of Information (VoI) Metric to Improve Sensemaking
Venue: arXiv cs.HC 2018
Year: 2018

Sensemaking is the cognitive process of extracting information, creating schemata from knowledge, making decisions from those schemata, and inferring conclusions. Human analysts are essential to exploring and quantifying this process, but they are limited by their inability to process the volume, variety, velocity, and veracity of data. Visualization tools are essential for helping this human-computer interaction. For example, analytical tools that use graphical linknode visualization can help sift through vast amounts of information. However, assisting the analyst in making connections with visual tools can be challenging if the information is not presented in an intuitive manner. Experimentally, it has been shown that analysts increase the number of hypotheses formed if they use visual analytic capabilities. Exploring multiple perspectives could increase the diversity of those hypotheses, potentially minimizing cognitive biases. In this paper, we discuss preliminary research results that indicate an improvement in sensemaking over the traditional link-node visualization tools by incorporating an annotation enhancement that differentiates links connecting nodes. This enhancement assists by providing a visual cue, which represents the perceived value of reported information. We conclude that this improved sensemaking occurs because of the removal of the limitations of mentally consolidating, weighing, and highlighting data. This study aims to investigate whether line thickness can be used as a valid representation of VoI.
Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data
Venue: arXiv cs.LG 2018
Year: 2018

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized logistic regression (with and without stability selection), and a recurrent neural network are trained on two years of adult inpatient EHR data (3,387 attributes for 34,505 patients who generated 90,013 training samples with 5,618 cases and 84,395 controls). Predictions are internally evaluated with 50 iterations of 5-fold grouped cross-validation with special emphasis on calibration, an analysis of which is performed at the patient as well as hospitalization level. Error is assessed with respect to diagnosis, race, age, gender, AKI identification method, and hospital utilization. In an additional experiment, the regularization penalty is severely increased to induce parsimony and interpretability. Predictors identified for rehospitalized patients are also reported with a special analysis of medications that might be modifiable risk factors. Insights from this study might be used to construct a predictive tool for AKI in rehospitalized patients. An accurate estimate of AKI risk at hospital entry might serve as a prior for an admitting provider or another predictive algorithm.
A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time
Venue: arXiv cs.DS 2018
Year: 2018

We consider the classic scheduling problem of minimizing the total weighted flow-time on a single machine (min-WPFT), when preemption is allowed. In this problem, we are given a set of $n$ jobs, each job having a release time $r_j$, a processing time $p_j$, and a weight $w_j$. The flow-time of a job is defined as the amount of time the job spends in the system before it completes; that is, $F_j = C_j - r_j$, where $C_j$ is the completion time of job. The objective is to minimize the total weighted flow-time of jobs. This NP-hard problem has been studied quite extensively for decades. In a recent breakthrough, Batra, Garg, and Kumar presented a {\em pseudo-polynomial} time algorithm that has an $O(1)$ approximation ratio. The design of a truly polynomial time algorithm, however, remained an open problem. In this paper, we show a transformation from pseudo-polynomial time algorithms to polynomial time algorithms in the context of min-WPFT. Our result combined with the result of Batra, Garg, and Kumar settles the long standing conjecture that there is a polynomial time algorithm with $O(1)$-approximation for min-WPFT.
Adaptive Spatial Modulation for Visible Light Communications with an Arbitrary Number of Transmitters
Venue: arXiv cs.IT 2018
Year: 2018

As a power and bandwidth efficient modulation scheme, the optical spatial modulation (SM) technique has recently drawn increased attention in the field of visible light communications (VLC). To guarantee the number of bits mapped by the transmitter's index at each timeslot is an integer, the number of transmitters (i.e., light-emitting diodes) in the SM based VLC system is often set be a power of two. To break the limitation on the required number of transmitters and provide more design flexibility, this paper investigates the SM based VLC with an arbitrary number of transmitters. Initially, a channel adaptive bit mapping (CABM) scheme is proposed, which includes three steps: bit mapping in space domain, bit mapping in signal domain, and the channel adaptive mapping. The proposed CABM scheme allows operation with an arbitrary number of transmitters, and is verified to be an efficient scheme through numerical results. Based on the CABM scheme, the information-theoretical aspects of the SM based VLC are analyzed. The theoretical expression of the mutual information is first analyzed. However, it is very hard to evaluate system performance. To obtain more insights, a lower bound of the mutual information is derived, which is in closedform. Both theoretical analysis and numerical results show that the gap between the mutual information and its lower bound is small. Finally, to further improve the system performance, the precoding scheme is proposed for the SM based VLC. Numerical results show that the system performance improves dramatically when using the proposed precoding scheme.
The Strongly Asynchronous Massive Access Channel
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a Strongly Asynchronous and Slotted Massive Access Channel (SAS-MAC) where $K_n:=e^{nν}$ different users transmit a randomly selected message among $M_n:=e^{nR}$ ones within a strong asynchronous window of length $A_n:=e^{nα}$ blocks, where each block lasts $n$ channel uses. A global probability of error is enforced, ensuring that all the users' identities and messages are correctly identified and decoded. Achievability bounds are derived for the case that different users have similar channels, the case that users' channels can be chosen from a set which has polynomially many elements in the blocklength $n$, and the case with no restriction on the users' channels. A general converse bound on the capacity region and a converse bound on the maximum growth rate of the number of users are derived.
Multi-Agent Generative Adversarial Imitation Learning
Venue: arXiv cs.LG 2018
Year: 2018

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse reinforcement learning. We further introduce a practical multi-agent actor-critic algorithm with good empirical performance. Our method can be used to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents.
On Strengthening the Logic of Iterated Belief Revision: Proper Ordinal Interval Operators
Venue: arXiv cs.AI 2018
Year: 2018

Darwiche and Pearl's seminal 1997 article outlined a number of baseline principles for a logic of iterated belief revision. These principles, the DP postulates, have been supplemented in a number of alternative ways. Most of the suggestions made have resulted in a form of `reductionism' that identifies belief states with orderings of worlds. However, this position has recently been criticised as being unacceptably strong. Other proposals, such as the popular principle (P), aka `Independence', characteristic of `admissible' revision operators, remain commendably more modest. In this paper, we supplement both the DP postulates and (P) with a number of novel conditions. While the DP postulates constrain the relation between a prior and a posterior conditional belief set, our new principles notably govern the relation between two posterior conditional belief sets obtained from a common prior by different revisions. We show that operators from the resulting family, which subsumes both lexicographic and restrained revision, can be represented as relating belief states that are associated with a `proper ordinal interval' (POI) assignment, a structure more fine-grained than a simple ordering of worlds. We close the paper by noting that these operators satisfy iterated versions of a large number of AGM era postulates, including Superexpansion, that are not sound for admissible operators in general.
Pythia v0.1: the Winning Entry to the VQA Challenge 2018
Venue: arXiv cs.CV 2018
Year: 2018

This document describes Pythia v0.1, the winning entry from Facebook AI Research (FAIR)'s A-STAR team to the VQA Challenge 2018. Our starting point is a modular re-implementation of the bottom-up top-down (up-down) model. We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, fine-tuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2.0 dataset -- from 65.67% to 70.22%. Furthermore, by using a diverse ensemble of models trained with different features and on different datasets, we are able to significantly improve over the 'standard' way of ensembling (i.e. same model with different random seeds) by 1.31%. Overall, we achieve 72.27% on the test-std split of the VQA v2.0 dataset. Our code in its entirety (training, evaluation, data-augmentation, ensembling) and pre-trained models are publicly available at: https://github.com/facebookresearch/pythia
Colored range closest-pair problem under general distance functions
Venue: arXiv cs.CG 2018
Year: 2018

The range closest-pair (RCP) problem is the range-search version of the classical closest-pair problem, which aims to store a given dataset of points in some data structure such that when a query range $X$ is specified, the closest pair of points contained in $X$ can be reported efficiently. A natural generalization of the RCP problem is the {colored range closest-pair} (CRCP) problem in which the given data points are colored and the goal is to find the closest {bichromatic} pair contained in the query range. All the previous work on the RCP problem was restricted to the uncolored version and the Euclidean distance function. In this paper, we make the first progress on the CRCP problem. We investigate the problem under a general distance function induced by a monotone norm; in particular, this covers all the $L_p$-metrics for $p > 0$ and the $L_\infty$-metric. We design efficient $(1+\varepsilon)$-approximate CRCP data structures for orthogonal queries in $\mathbb{R}^2$, where $\varepsilon>0$ is a pre-specified parameter. The highlights are two data structures for answering rectangle queries, one of which uses $O(\varepsilon^{-1} n \log^4 n)$ space and $O(\log^4 n + \varepsilon^{-1} \log^3 n + \varepsilon^{-2} \log n)$ query time while the other uses $O(\varepsilon^{-1} n \log^3 n)$ space and $O(\log^5 n + \varepsilon^{-1} \log^4 n + \varepsilon^{-2} \log^2 n)$ query time. In addition, we also apply our techniques to the CRCP problem in higher dimensions, obtaining efficient data structures for slab, 2-box, and 3D dominance queries. Before this paper, almost all the existing results for the RCP problem were achieved in $\mathbb{R}^2$.
Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output, and ranks them according to likelihood. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23,000 vehicles traversing five different intersections, collected using a vehicle mounted Lidar based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.
FINJ: A Fault Injection Tool for HPC Systems
Venue: arXiv cs.DC 2018
Year: 2018

We present FINJ, a high-level fault injection tool for High-Performance Computing (HPC) systems, with a focus on the management of complex experiments. FINJ provides support for custom workloads and allows generation of anomalous conditions through the use of fault-triggering executable programs. FINJ can also be integrated seamlessly with most other lower-level fault injection tools, allowing users to create and monitor a variety of highly-complex and diverse fault conditions in HPC systems that would be difficult to recreate in practice. FINJ is suitable for experiments involving many, potentially interacting nodes, making it a very versatile design and evaluation tool.
Automatic Detection of Node-Replication Attack in Vehicular Ad-hoc Networks
Venue: arXiv cs.LG 2018
Year: 2018

Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as low as possible. In this type networks, traditional detection algorithms of node replication attacks are not efficient. In this paper, we propose an initial idea to apply a newly adapted statistical methodology that can detect node replication attacks with high performance as compared to state-of-the-art techniques. We provide a sufficient description of this methodology and a road-map for testing and experiment its performance.
Performance Analysis of User-centric Virtual Cell Dense Networks over mmWave Channels
Venue: arXiv cs.IT 2018
Year: 2018

This paper analyzes the ergodic capacity of a user-centric virtual cell (VC) dense network, where multiple access points (APs) form a VC for each user equipment (UE) and transmit data cooperatively over millimeter wave (mmWave) channels. Different from traditional microwave radio communications, blockage phenomena have an important effect on mmWave transmissions. Accordingly, we adopt a distance-dependent line-of-sight (LOS) probability function and model the locations of the LOS and non-line-of-sight (NLOS) APs as two independent non-homogeneous Poisson point processes (PPP). Invoking this model in a VC dense network, new expressions are derived for the downlink ergodic capacity, accounting for: blockage, small-scale fading and AP cooperation. In particular, we compare the ergodic capacity for different types of fading distributions, including Rayleigh and Nakagami. Numerical results validate our analytical expressions and show that AP cooperation can provide notable capacity gain, especially in low-AP-density regions.
Adapting control policies from simulation to reality using a pairwise loss
Venue: arXiv cs.RO 2018
Year: 2018

This paper proposes an approach to domain transfer based on a pairwise loss function that helps transfer control policies learned in simulation onto a real robot. We explore the idea in the context of a 'category level' manipulation task where a control policy is learned that enables a robot to perform a mating task involving novel objects. We explore the case where depth images are used as the main form of sensor input. Our experimental results demonstrate that proposed method consistently outperforms baseline methods that train only in simulation or that combine real and simulated data in a naive way.
Emerging from The Cloud: A Bibliometric Analysis of Cloud Forensics Studies
Venue: arXiv cs.CR 2018
Year: 2018

The emergence of cloud computing technologies has changed the way we store, retrieve, and archive our data. With the promise of unlimited, reliable and always-available storage, a lot of private and confidential data are now stored on different cloud platforms. Being such a gold mine of data, cloud platforms are among the most valuable targets for attackers. Therefore, many forensics investigators have tried to develop tools, tactics and procedures to collect, preserve, analyse and report evidences of attackers activities on different cloud platforms. Despite the number of published articles there is not a bibliometric study that presents cloud forensics research trends. This paper aims to address this problem by providing a comprehensive assessment of cloud forensics research trends between 2009 and 2016. Moreover, we provide a classification of cloud forensics process to detect the most profound research areas and highlight remaining challenges.
Distributed leader election and computation of local identifiers for programmable matter
Venue: arXiv cs.DC 2018
Year: 2018

The context of this paper is programmable matter, which consists of a set of computational elements, called particles, in an infinite graph. The considered infinite graphs are the square, triangular and king grids. Each particle occupies one vertex, can communicate with the adjacent particles, has the same clockwise direction and knows the local positions of neighborhood particles. Under these assumptions, we describe a new leader election algorithm affecting a variable to the particles, called the k-local identifier, in such a way that particles at close distance have each a different k-local identifier. For all the presented algorithms, the particles only need a O(1)-memory space.